import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

def add_layer(inputs,in_size,out_size,activation_function=None,):
	Weights = tf.Variable(tf.random_normal([in_size,out_size]))
	biases = tf.Variable(tf.zeros([1,out_size])+0.1)
	Wx_plus_b = tf.matmul(inputs,Weights)+biases
	if activation_function is None:
		outputs = Wx_plus_b
	else:
		outputs = activation_function(Wx_plus_b)
	return outputs
def compute_accuracy(v_xs,v_ys):
	global prediction
	y_pre = sess.run(prediction,feed_dict={xs:v_xs})
	correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
	accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
	result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
	return result



xs = tf.placeholder(tf.float32,[None,784])#28
ys = tf.placeholder(tf.float32,[None,10])

prediction = add_layer(xs, 784, 10,activation_function=tf.nn.softmax)


cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))  #loss-->A alogrithm of cross_entropy
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()

sess.run(tf.global_variables_initializer())


for i in range(1000):
	batch_xs,batch_ys = mnist.train.next_batch(100)

	#print(batch_xs.shape)
	sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
	if i%50 == 0:
		print(compute_accuracy(mnist.test.images,mnist.test.labels))
